We build the future workforce
AI workers that replace specific roles — sales, QA, support — and run inside the tools your team already uses. Pre-built for common roles, custom for the rest.
What an AI worker is
Hired, not integrated
Replaces a role, not a tool
Each worker takes a defined seat — SDR, QA analyst, L1 support — with a job description and KPIs.
Speaks your customer's language
Built on a multilingual moat across CIS languages where general models drop accuracy and tone.
Lives inside your stack
Logs into your CRM, dialer, or helpdesk as a real user — no new dashboard for your team to learn.
Products
Pre-built AI workers, ready to hire
Three workers that replace specific roles. Built for CIS markets, deployed into your existing stack.
In pilots with 7 teams across CIS markets.
Aziza
Replaces SDR & Sales Manager
A CRM-native sales worker. Qualifies leads, runs follow-ups, and books meetings inside Bitrix24, amoCRM, HubSpot, Zoho, or your custom CRM via API — logged in as a real user.
Visit aziza.lookona.comOvozly
Replaces a QA analyst at call centers
Listens to every call, scores it against your QA rubric, and flags the calls a human needs to review. Handles mixed-language calls natively.
Visit ovozly.comAli
Replaces an L1 customer support agent
Handles tier-1 customer questions across chat and email, escalates the rest. Same multilingual coverage. Currently in development.
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Two ways to put an AI worker on your team
Hire one of our pre-built workers, or commission a custom one for a role we haven't built yet.
Pre-built AI Workers
Aziza, Ovozly, and Ali — pre-built workers for sales, QA, and support. Configured to your workflow, deployed into your stack, priced by seat.
Sales · QA · Customer support
Custom AI Worker Builds
When the role you need isn't on the shelf, we build it. Same autopilot framing — defined job, multilingual where it matters, lives inside your existing tools.
Scoping, build, integration, handoff
How we engage
We start with a scoped pilot. If it works, we scale. If it doesn't, you know early.
How we think
AI workers, not AI demos
We build AI workers that take a defined seat and ship the work — not demos or proof-of-concepts that stall before reaching a real workflow.
Every project balances technical depth with practical constraints: timelines, budgets, and team readiness.
Evidence over hype
We benchmark models on your data before recommending them. No defaults, no hand-waving.
Multilingual depth
We work with languages and domains where general-purpose models struggle — low-resource languages, domain-specific terminology, mixed-script inputs.
Maintainable systems
We hand off documented, testable code — not black-box notebooks your team can't modify.
What we believe
AI should fit the workflow, not replace it
Most AI projects fail at integration, not at model quality. We focus on the part that actually matters — making it work inside your team's existing tools and processes.
See our delivery processMap before building
We audit the actual workflow first — where time is lost, what decisions repeat, what data already exists.
Pick the right model, not the biggest
A fine-tuned small model often beats a general-purpose large one on cost, speed, and accuracy for specific tasks.
Measure after shipping
We define success metrics before writing code and track them after launch. If the numbers don't move, we adjust.
Process
Four steps. No surprises
Every project follows the same structure so you always know where things stand.
Scope
Define the problem, agree on success criteria, and choose the right approach.
Prototype
Build a working proof on real data. Test with your team, not in isolation.
Integrate
Connect to your systems, handle edge cases, and set up monitoring.
Handoff
Document everything, train your team, and make sure it runs without us.
Team
Real people, AI workforce behind them
We're a small team on purpose. We use AI tooling across research, coding, testing, and content — so we move fast without scaling headcount.
Founders
Systems & Engineering
Behzod Ortiqov
MSc in Applied Mathematics and Physics. 6 years in machine learning, data science, and MLOps across MedTech and FinTech. Handles architecture, implementation, and integration.
LinkedIn
Product & Delivery
Muhammad Abdugafarov
MSc in Information Technologies in Economy. 8 years in enterprise software development and engineering leadership, with applied AI for finance. Runs product direction, client communication, and delivery.
LinkedInAI tooling
We use AI tools across the entire workflow — research, code generation, language QA, testing, and documentation. This lets two people deliver what typically requires a larger team.
Contact
Have a problem AI might solve?
Tell us what you're working on. We respond within 24 hours.